Volumetric measurement

ABSTRACT

Disclosed is a method of determining a volume of liquid in a sample tube, comprising the steps of capturing an image of the sample tube, determining a first region of interest within the sample tube based upon pre-stored information concerning dimensional properties of the sample tube, scanning the first region of interest to detect the position of a meniscus indicative of an upper extent of the liquid, and using said meniscus position together with certain pre-stored properties of the sample tube to determine a volume of liquid in the tube, and outputting said volume. Also disclosed is an apparatus for performing the method.

BACKGROUND

In systems which store quantities of liquid samples, e.g. blood samples,chemical samples or other biological samples, robotically operatedautomated storage systems are commonly used and these can store manythousands or even millions of individual samples in small tubes. Accessto the individual tubes is required periodically and it is useful forthe operator of such a system to have an indication of the amount ofsample stored in a particular tube so that as quantities of the sampleare removed over time, an ongoing check can be kept on the volume stillavailable.

Individual sample tubes are typically configured to have a maximumvolume of a few millilitres, with typical volumes being 0.3 ml, 0.75 ml,1.4 ml and 2 ml. A given quantity of tubes is normally stored in a rackwhich can hold a certain quantity of tubes, e.g. 96 tubes. One storagesystem can comprise a variety of different tube sizes.

A prior art solution to the problem of assessing volume involves themanual inspection of a particular tube to assess the volume remaining,and this may be supplemented by an estimate from a user. Obviously sucha solution is very labour intensive and may not be used when a greatquantity of tubes requires verification.

Other, more automated, solutions exist. One of these involves theaccurate weighing of a tube, which can give the weight of the sampleonce the nominal weight of the empty tube is subtracted therefrom.However, this can be a time consuming task and requires individual tubesto be assessed separately.

There are devices available which aim to expedite this process. One suchdevice processes a rack of tubes by selecting a particular tube, readingits identifying bar code and then weighing it. The resulting data isthen stored in a file which can be reconciled with the inventory of theentire stock of tubes. Such devices can also be used to pre-weigh thetubes so that the later weight calculation is relatively easy to do.However, this further complicates the inventory system. Also, suchdevices tend to be quite slow in operation and can take between 20 and30 minutes to individually weigh a rack consisting of 96 tubes.Furthermore, if individual tubes are not pre-weighed there is a questionabout how accurate the subsequent volume estimating can be given thatthere is a noticeable difference in the weight of individual tubes andit has been seen that these can vary by as much as 20 mg.

An alternative approach to using weight to infer a volume in a tube isto utilize a non-contact liquid level detection. This approach uses oneor more sensors which are operable to determine the distance between thesensor and the surface of the liquid in a tube. By use of a suitablyknown tube, the level of the liquid may be used to determine the volumeof sample in the tube. An advantage of such a sensor is that it is ableto operate at a higher speed than the weighing solution discussedpreviously. However, a particular shortcoming of such a device is thatthe tube cap or septa must be removed in order for the upper level ofthe liquid to be exposed. In addition to increasing the risk of samplecross contamination, this step has major implications for samplequality, unless it is performed in a controlled environment, e.g. a lowhumidity environment, to prevent the uptake of moisture, which could, ofcourse, upset the volume calculations.

SUMMARY

There therefore exists a desire to provide means by which relativelyhigh speed calculations of remaining volumes of samples in a pluralityof tubes can be made, whilst minimizing the risk of contamination orother degradation of sample quality. It is an aim of the preferredembodiments disclosed herein to address the shortcomings in the priorart, whether these are set out in detail above or not.

According to aspects of the preferred embodiment there is provided anapparatus and method as set forth in the appended claims. Other featuresof the inventive apparatus and method will be apparent from thedependent claims, and the description which follows.

BRIEF DESCRIPTION OF THE DRAWINGS

Aspects of the preferred embodiment will be described in reference tothe Drawing, where like numerals reflect like elements:

FIG. 1 shows an apparatus according to an embodiment of the disclosure;

FIG. 2 shows a flowchart related to the operation of an embodiment ofthe disclosure;

FIG. 3 shows further details of particular steps in the flowchart ofFIG. 2; and

FIG. 4 shows a sample captured image.

DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENT

FIG. 1 shows a schematic view of an embodiment of a preferred embodimentof the apparatus. The apparatus comprises a robotic gripper 10, which isoperable to pick up one or more tubes 100 from within a rack 110. Therack 110 comprises a plurality of tubes 100, arranged in a rectangularmatrix arrangement.

The rack 110 is movable from a stowed position 112 to an accessibleposition 114, from which the gripper 10 can access the tubes 100. Theaccessible position may be located within a controlled environmentalchamber 116.

An alternative embodiment may be provided which is operable only toassess volumes and is not integrated with any other apparatus. Such adevice is operable to receive a rack of tubes, to analyze each tube, andreturn them to the rack. The rack can then be returned to its stowedlocation or processed as required.

The robotic gripper mechanism is known in the art, and no specialknowledge of this device is necessary in order to comprehend embodimentsof the disclosure.

Once a particular tube is requested, the gripper 10 is instructed togrip the desired tube and transport it to a further device, where it maybe stored or processed in some way. Once the tube 100 is removed fromthe rack, it is momentarily suspended between a camera 20 and lightsource 30. The camera captures an image of the tube, illuminated frombehind, and the gripper 10 then proceeds to process the tube asrequested. There is a small delay whilst the tube's image is captured inthis way.

The system is controlled via a suitable programmed computer 1, which isoperable to control the gripper 10, and to process the captured imagefrom the camera 20. The computer 1 can also maintain a database of tubevolumes which can be updated from time to time, as required.

Once the image has been captured and stored on the computer 1, the imageis analyzed to determine the position of the meniscus or upper level ofthe liquid in the tube. The level of the meniscus can then be used toinfer the volume of liquid in the tube. The process will now bedescribed in more detail with reference to the flowcharts shown in FIGS.2 and 3.

FIG. 2 is a flowchart showing the major steps in the volume calculationprocess. At step 200, the tube to be assessed is presented to the camera20 by means of the gripper mechanism 10. The tube 100 is positionedbetween a light source 30 and the camera 20 so that the tube 100 isbacklit. In some instances, if the samples are not essentiallytransparent, then an additional or alternative light source may beactivated. The alternative light source may be positioned to light thetube 100 from the front, the side or any other suitable direction.

At step 210, the image is captured using the camera 20. The camera 20may be any suitable imaging device and may comprise a regular digitalcamera, having suitable resolution. For example, one such suitableresolution is 1628×1236 pixels. The camera is activated once the grippermechanism 10 has indicated to the computer 1 that it is holding the tube100 stationary in the correct position. The computer is then able tocross-reference the image and the particular tube 100 in its database.

At step 220, the image is processed to locate various datum points.These may be physical markers, located in the background of the imagingposition. They can comprise crosshairs, dots or any other suitablemarker which the image processing software may use to frame theresultant image. Once the datum points are located, the position of thetube 100 can be accurately determined in relation to these.

At Step 230, the image may be corrected for perspective if required.This may be useful in case the camera 20 and/or the tube 100 aremis-aligned during the image capture step 210.

Step 240 represents the fact that multiple tubes may be processed insequence as the result of a single pick-up operation by gripper 10. Thegripper may be configured to pick up an entire row or column of tubesfrom a rack. In such a scenario, the camera 30 is operable to image allthe selected tubes in a single image. By use of the datum points,already mentioned, the individual tubes can be recognised and processedin turn.

The iterative process represented by steps 240 and 250 is shown in moredetail in FIG. 3, which shows the steps performed for each tubeidentified.

At step 241, the image processing software generates regions ofinterest, based on the tube type and its position within the overallimaged frame. These regions of interest correspond to areas whichrequire further processing to locate, e.g. the meniscus.

At step 242, the presence or absence of the tube cap is detected. If thecap is missing, then a flag can be set alongside the data for thatparticular tube to indicate that there may be a problem with thatparticular sample. It could be that a mechanical error has occurred,resulting in the loss of the cap, and the sample may be compromised.

At step 243, the image processing software uses an edge-detectionalgorithm to locate, within the region of interest corresponding to thebody of the tube, the upper level of the liquid in the tube, ormeniscus. Known edge-detection algorithms may be used for this purpose.

At step 244, the meniscus level determined in the previous step is usedto calculate the volume of liquid in the tube. The preferred method ofdoing this is to use a data look-up table arranged to correlate a givenmeniscus level with a pre-determined volume which has either beenmeasured or calculated previously.

The image processing software is either informed what tube type is inuse or is able to distinguish different tube types from each other.Consequently, two variables may be passed to the look up table:tube_type_identifier and meniscus_level. In this way an accurate volumecan be given which is tailored to a particular tube type.

At step 245, the image processing software is operable to generate afurther region of interest located towards the lower end of the tube. Byseeking an edge or difference in colour or brightness in this region, itis possible to infer that the sample includes a quantity of precipitate.

The region of interest defined in step 245 is arranged to be somewhatsmaller than the volume of liquid measured in the tube. In this way, itis possible to ensure that only precipitates positioned lower than themeasured meniscus are detected, avoiding false positive detections.

Users of embodiments of the disclosed device and method are notgenerally interested in the quantity of precipitate: the mere presenceof solid material in the sample is usually sufficient information totrigger possible further investigation if required. However, in somecases, an indication of the volume of precipitate can be useful, andusing the same look up table technique, it is possible to determine avolume of precipitate and log this value.

The steps 241-246 are repeated as many times as there are tubes in aparticular pick-up operation.

With further reference to FIG. 2, at step 260 the data generated in thepreceding steps is output, either to be stored in a database for furtheranalysis, or in real-time to a user.

The format of the data can be set according to the preference of theuser, but it may be in the format:

TUBE_ID, CAP_PRESENT(YES/NO), VOLUME, PRECIPITATE(VOLUME or YES/NO)

At step 270, the tube 100 is removed from the imaging system andcontinues to its next destination, which may be a further process orstorage, for instance. Alternatively, in the benchtop embodiment of theinvention, the tube(s) is/are simply returned to the rack.

FIG. 4 shows a sample image captured from camera 30. The datum points220 are clearly visible in each corner of the image. These are used toframe the image and as a basis for calculating all subsequent distances.By use of the datum points 220, the image processing software is able todetermine the expected position of one or more tubes in the gripper 10.

FIG. 4 shows the gripper 10 and six tubes of varying volume. The threeleftmost tubes include a precipitate and the three rightmost tubes donot.

In order to assess the volume of a particular tube, a first region ofinterest 200 is defined. This region of interest is based upon theposition of the tube 100 within the apparatus, this position beingdetermined in relation to the datum points 220.

In order to determine the level of the meniscus within the region 200,the image processing software is operable to scan in a downwardsdirection from the top of the region 210 until an edge is detected. Edgedetection algorithms per se are well known and any suitable algorithmmay be used.

Once a first edge is detected in this way, it is registered as themeniscus position, and a corresponding volume can be determined as hasbeen described herein.

The next step involved defining a second region of interest 210 fordetecting a possible precipitate. This region is defined to lie withinthe first region, begins just below the meniscus and extends downtowards the bottom of the tube. This region 210 is then scanned in anattempt to locate a further edge or contrast shift, indicative of thepresence of a precipitate. No attempt is made to determine the volume ofthe precipitate and a simple yes/no determination is made.

As noted above, it is also possible to determine from the image whethera particular tube is capped or not. The image processing software isarranged to examine an area at the top of the tube to determine thepresence or absence of a cap. The result of this determination is outputalong with the other data. By examining the area in which a cap isexpected to be, the absence of a cap is quite apparent and easy todetermine. However, it is also possible to determine an incorrectlyfitted cap if it is present outside of the expected area and this can bereported to the user.

In the specific example shown in FIG. 4, it can be seen that the tubehas a ‘neck’ region 230 partway along its length. This neck region couldpossibly interfere with determining the position of the meniscus. Othertube configurations may have similar features which could complicate theprocess.

As a first attempt, the region of interest 200 may be searched above andthen below the neck 230 and if no meniscus is found, then it may beinferred that the meniscus is located in or near the neck region 230. Inthis case, further steps may be taken to focus in on that region. Forinstance, a ‘mask’ image may be available which can effectively besubtracted from the captured image, said mask image being arepresentation of an empty tube. This approach can reveal the presenceof the meniscus more clearly. Another approach would be to median-filterthe neck region 230 so that thin edges in the horizontal direction canbe removed, thereby revealing the meniscus which has a generally thickeredge. Other techniques may be possible depending on the particularconfiguration of tube.

An aim of the image processing process is to maintain a relatively lowlevel of computational complexity, unless further effort is required.For instance, if the meniscus can be easily determined, then the basicalgorithm is used. If the meniscus cannot be located, then it ispossible that the tube is empty or that the meniscus is obscured by theneck region 230, for instance. In that case, then one or more furtheroperations are performed to attempt to locate the meniscus and return avalue for the volume.

An optional feature, which may be added to the system, is a barcodereader which is able to read the unique barcode on a given tube or rack,so that little or no manual intervention is required once the rack oftubes has been loaded. The bar code reader can be located as required inorder to scan the bar codes of the tubes or rack as the rack is loaded.

Embodiments of the disclosed system can quickly and accurately determinethe volume of one or more tubes from a standard rack of tubes. The tubescan remain capped at all times, thus minimising or avoiding the risk ofcontamination.

Attention is directed to all papers and documents which are filedconcurrently with or previous to this specification in connection withthis application and which are open to public inspection with thisspecification, and the contents of all such papers and documents areincorporated herein by reference.

All of the features disclosed in this specification (including anyaccompanying claims, abstract and drawings), and/or all of the steps ofany method or process so disclosed, may be combined in any combination,except combinations where at least some of such features and/or stepsare mutually exclusive.

Each feature disclosed in this specification (including any accompanyingclaims, abstract and drawings) may be replaced by alternative featuresserving the same, equivalent or similar purpose, unless expressly statedotherwise. Thus, unless expressly stated otherwise, each featuredisclosed is one example only of a generic series of equivalent orsimilar features.

The invention is not restricted to the details of the foregoingembodiments. The invention extends to any novel embodiment orcombination, of the features disclosed in this specification (includingany accompanying claims, abstract and drawings), or to any novelembodiment or combination, of the steps of any method or process sodisclosed.

1. A method of determining a volume of liquid in a sample tube,comprising the steps of: capturing an image of the sample tube;determining a first region of interest within the sample tube based uponpre-stored information concerning dimensional properties of the sampletube; scanning the first region of interest to detect the position of ameniscus indicative of an upper extent of the liquid; determining avolume of liquid in the tube by using said meniscus position togetherwith certain pre-stored properties of the sample tube; and outputtingsaid volume.
 2. The method of claim 1 wherein the step of scanning thefirst region of interest comprises the use of an edge-detect algorithmto locate a discontinuity within the first region of interest.
 3. Themethod of claim 1 wherein the step of determining the volume comprisesinputting the measured meniscus position to a table of pre-stored datato determine a corresponding volume.
 4. The method of claim 1 whereinthe step of outputting said volume comprises outputting to a databasefor further analysis.
 5. The method of claim 1 wherein the step ofoutputting said volume comprises outputting via a display device to auser.
 6. The method of claim 1 comprising the further steps of: defininga second region of interest, positioned below the meniscus position;scanning said second region of interest to detect a possiblediscontinuity in the second region of interest; and recording thepresence or absence of a precipitate depending upon the presence orabsence of a discontinuity.
 7. The method of claim 1 comprising thefurther steps of detecting the presence or absence of a cap on thesample tube and recording the cap presence or absence result.
 8. Themethod of claim 1 comprising the further steps of reading a bar codeassociated with one or more sample tubes and recording the result. 9.The method of claim 1 wherein the step of capturing an image of thesample tube includes capturing the image of a plurality of sample tubessimultaneously.
 10. An apparatus operable to determine a volume ofliquid in a sample tube, comprising: a camera for capturing an image ofthe sample tube; an image processor operable to determine a first regionof interest within the sample tube based upon pre-stored informationconcerning dimensional properties of the sample tube, scan the firstregion of interest to detect the position of a meniscus, indicative ofan upper extent of the liquid, determine a volume of liquid in the tubeby using said meniscus position together with certain pre-storedproperties of the sample tube, and output said volume.
 11. The apparatusof claim 10 wherein the image processor is operable to scan the firstregion of interest, and to use an edge-detect algorithm to locate adiscontinuity within the first region of interest.
 12. The apparatus ofclaim 10 wherein the image processor is operable to input the measuredmeniscus position to a table of pre-stored data to determine acorresponding volume.
 13. The apparatus of claim 10 wherein the imageprocessor is operable to output to a database for further analysis. 14.The apparatus of claim 10 wherein the image processor is operable tooutput via a display device to a user.
 15. The apparatus of claim 10wherein the image processor is operable to define a second region ofinterest positioned below the meniscus position; scan said second regionof interest to detect a possible discontinuity in the second region ofinterest, and record the presence or absence of a precipitate dependingupon the presence or absence of a discontinuity.
 16. The apparatus ofclaim 10 wherein the image processor is operable to detect the presenceor absence of a cap on the sample tube, and to record the cap presenceor absence result.
 17. The apparatus of claim 10 further comprising abar code reader for reading a bar code associated with one or moresample tubes.
 18. The apparatus of claim 10 operable to capture aplurality of sample tube images simultaneously.